whisperX/whisperx/vad.py

import pandas as pd
import numpy as np
from pyannote.core import Annotation, Segment, SlidingWindowFeature, Timeline
from typing import List, Tuple, Optional

class Binarize:
    """Binarize detection scores using hysteresis thresholding
    Parameters
    ----------
    onset : float, optional
        Onset threshold. Defaults to 0.5.
    offset : float, optional
        Offset threshold. Defaults to `onset`.
    min_duration_on : float, optional
        Remove active regions shorter than that many seconds. Defaults to 0s.
    min_duration_off : float, optional
        Fill inactive regions shorter than that many seconds. Defaults to 0s.
    pad_onset : float, optional
        Extend active regions by moving their start time by that many seconds.
        Defaults to 0s.
    pad_offset : float, optional
        Extend active regions by moving their end time by that many seconds.
        Defaults to 0s.
    max_duration: float
        The maximum length of an active segment, divides segment at timestamp with lowest score.
    Reference
    ---------
    Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of
    RNN-based Voice Activity Detection", InterSpeech 2015.

    Pyannote-audio
    """

    def __init__(
        self,
        onset: float = 0.5,
        offset: Optional[float] = None,
        min_duration_on: float = 0.0,
        min_duration_off: float = 0.0,
        pad_onset: float = 0.0,
        pad_offset: float = 0.0,
        max_duration: float = float('inf')
    ):

        super().__init__()

        self.onset = onset
        self.offset = offset or onset

        self.pad_onset = pad_onset
        self.pad_offset = pad_offset

        self.min_duration_on = min_duration_on
        self.min_duration_off = min_duration_off

        self.max_duration = max_duration

    def __call__(self, scores: SlidingWindowFeature) -> Annotation:
        """Binarize detection scores
        Parameters
        ----------
        scores : SlidingWindowFeature
            Detection scores.
        Returns
        -------
        active : Annotation
            Binarized scores.
        """

        num_frames, num_classes = scores.data.shape
        frames = scores.sliding_window
        timestamps = [frames[i].middle for i in range(num_frames)]

        # annotation meant to store 'active' regions
        active = Annotation()
        for k, k_scores in enumerate(scores.data.T):

            label = k if scores.labels is None else scores.labels[k]

            # initial state
            start = timestamps[0]
            is_active = k_scores[0] > self.onset
            curr_scores = [k_scores[0]]
            curr_timestamps = [start]
            for t, y in zip(timestamps[1:], k_scores[1:]):
                # currently active
                if is_active: 
                    curr_duration = t - start
                    if curr_duration > self.max_duration:
                        # if curr_duration > 15:
                            # import pdb; pdb.set_trace()
                        search_after = len(curr_scores) // 2
                        # divide segment
                        min_score_div_idx = search_after + np.argmin(curr_scores[search_after:])
                        min_score_t = curr_timestamps[min_score_div_idx]
                        region = Segment(start - self.pad_onset, min_score_t + self.pad_offset)
                        active[region, k] = label
                        start = curr_timestamps[min_score_div_idx]
                        curr_scores = curr_scores[min_score_div_idx+1:]
                        curr_timestamps = curr_timestamps[min_score_div_idx+1:]
                    # switching from active to inactive
                    elif y < self.offset:
                        region = Segment(start - self.pad_onset, t + self.pad_offset)
                        active[region, k] = label
                        start = t
                        is_active = False
                        curr_scores = []
                        curr_timestamps = []
                # currently inactive
                else:
                    # switching from inactive to active
                    if y > self.onset:
                        start = t
                        is_active = True
                curr_scores.append(y)
                curr_timestamps.append(t)

            # if active at the end, add final region
            if is_active:
                region = Segment(start - self.pad_onset, t + self.pad_offset)
                active[region, k] = label

        # because of padding, some active regions might be overlapping: merge them.
        # also: fill same speaker gaps shorter than min_duration_off
        if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0:
            if self.max_duration < float("inf"):
                raise NotImplementedError(f"This would break current max_duration param")
            active = active.support(collar=self.min_duration_off)

        # remove tracks shorter than min_duration_on
        if self.min_duration_on > 0:
            for segment, track in list(active.itertracks()):
                if segment.duration < self.min_duration_on:
                    del active[segment, track]

        return active


def merge_vad(vad_arr, pad_onset=0.0, pad_offset=0.0, min_duration_off=0.0, min_duration_on=0.0):

    active = Annotation()
    for k, vad_t in enumerate(vad_arr):
        region = Segment(vad_t[0] - pad_onset, vad_t[1] + pad_offset)
        active[region, k] = 1


    if pad_offset > 0.0 or pad_onset > 0.0 or min_duration_off > 0.0:
        active = active.support(collar=min_duration_off)
    
    # remove tracks shorter than min_duration_on
    if min_duration_on > 0:
        for segment, track in list(active.itertracks()):
            if segment.duration < min_duration_on:
                    del active[segment, track]
    
    active = active.for_json()
    active_segs = pd.DataFrame([x['segment'] for x in active['content']])
    return active_segs


if __name__ == "__main__":
    # from pyannote.audio import Inference
    # hook = lambda segmentation: segmentation
    # inference = Inference("pyannote/segmentation", pre_aggregation_hook=hook)
    # audio = "/tmp/11962.wav" 
    # scores = inference(audio)
    # binarize = Binarize(max_duration=15)
    # anno = binarize(scores)
    # res = []
    # for ann in anno.get_timeline():
    #     res.append((ann.start, ann.end))

    # res = pd.DataFrame(res)
    # res[2] = res[1] - res[0]
    import pandas as pd
    input_fp = "tt298650_sync.wav"
    df = pd.read_csv(f"/work/maxbain/tmp/{input_fp}.sad", sep=" ", header=None)
    print(len(df))
    N = 0.15
    g = df[0].sub(df[1].shift())
    input_base = input_fp.split('.')[0]
    df = df.groupby(g.gt(N).cumsum()).agg({0:'min', 1:'max'})
    df.to_csv(f"/work/maxbain/tmp/{input_base}.lab", header=None, index=False, sep=" ")
    print(df)
    import pdb; pdb.set_trace()